Automated assembly discrepancy feedback using 3D imaging and forward kinematics

Abstract Assembly of steel structures, modules, and pipe spools requires cycles of fitting and alignment in fabrication facilities and on construction sites. To minimize this work, good discrepancy feedback for automated refitting and realignment is required. A framework for such feedback is presented here that overcomes current limitations. It commences with a constrained registration step to overcome the incapabilities of the current discrepancy analysis approaches. By borrowing concepts from robotic kinematics and 3D image alignment theories, forward kinematics models are generated link by link, and thus provide the means for a local discrepancy analysis for quantifying the deviations autonomously. Experiments show that the proposed approach is suitably accurate and sufficiently fast to be employed for real-time feedback in order to systematically and automatically develop the realignment plans required for refitting and realigning assemblies, which is the key contribution of the work presented in this paper.

[1]  Ioannis Brilakis,et al.  Machine Vision-Based Concrete Surface Quality Assessment , 2010 .

[2]  Mahdi Safa,et al.  Automated Registration of 3D Point Clouds with 3D CAD Models for Remote Assessment of Staged Fabrication , 2014 .

[3]  Peter E.D. Love,et al.  Influence of Project Type and Procurement Method on Rework Costs in Building Construction Projects , 2002 .

[4]  Fei Dai,et al.  Comparison of Image-Based and Time-of-Flight-Based Technologies for Three-Dimensional Reconstruction of Infrastructure , 2013 .

[5]  Changmin Kim,et al.  Automated construction progress measurement using a 4D building information model and 3D data , 2013 .

[6]  Soon-Wook Kwon,et al.  Fitting range data to primitives for rapid local 3D modeling using sparse range point clouds , 2004 .

[7]  Frédéric Bosché,et al.  Automated retrieval of 3D CAD model objects in construction range images , 2008 .

[8]  Changmin Kim,et al.  Skeleton-based 3D reconstruction of as-built pipelines from laser-scan data , 2013 .

[9]  C. Kim,et al.  Knowledge-Based Approach for 3D Reconstruction of As-Built Industrial Plant Models from Laser-Scan Data , 2013 .

[10]  George Vosselman,et al.  An integrated approach for modelling and global registration of point clouds , 2007 .

[11]  Hojjat Adeli,et al.  A New Approach for Health Monitoring of Structures: Terrestrial Laser Scanning , 2007, Comput. Aided Civ. Infrastructure Eng..

[12]  Burcu Akinci,et al.  A formalism for utilization of sensor systems and integrated project models for active construction quality control , 2006 .

[13]  Ioannis Brilakis,et al.  Progressive 3D reconstruction of infrastructure with videogrammetry , 2011 .

[14]  Patricio A. Vela,et al.  Generating Absolute-Scale Point Cloud Data of Built Infrastructure Scenes Using a Monocular Camera Setting , 2015 .

[15]  S. V. Sreenivasan,et al.  A framework for rapid local area modeling for construction automation , 2002 .

[16]  Carl T. Haas,et al.  Rapid Geometric Modeling for Unstructured Construction Workspaces , 2003 .

[17]  Ioannis K. Brilakis,et al.  A videogrammetric as-built data collection method for digital fabrication of sheet metal roof panels , 2013, Adv. Eng. Informatics.

[18]  Gaurav S. Sukhatme,et al.  Multi-image stitching and scene reconstruction for evaluating defect evolution in structures , 2011 .

[19]  David Fofi,et al.  A review of recent range image registration methods with accuracy evaluation , 2007, Image Vis. Comput..

[20]  Bon-Gang Hwang,et al.  Measuring the Impact of Rework on Construction Cost Performance , 2009 .

[21]  AhmedMahmoud,et al.  Using digital photogrammetry for pipe-works progress tracking11This paper is one of a selection of papers in this Special Issue on Construction Engineering and Management. , 2012 .

[22]  E. S. Slaughter,et al.  Simulation of structural steel erection to assess innovations , 1997 .

[23]  Lucio Soibelman,et al.  Shape-Based Retrieval of Construction Site Photographs , 2008 .

[24]  Carl T. Haas,et al.  Automated 3D compliance checking in pipe spool fabrication , 2014, Adv. Eng. Informatics.

[25]  Burcu Akinci,et al.  Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: A Review of Related Techniques | NIST , 2010 .

[26]  Mani Golparvar-Fard,et al.  Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques , 2011 .

[27]  David J. Edwards,et al.  A rework reduction model for construction projects , 2004, IEEE Transactions on Engineering Management.

[28]  Burcu Akinci,et al.  Deviation analysis method for the assessment of the quality of the as-is Building Information Models generated from point cloud data , 2013 .

[29]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[30]  Burcin Becerik-Gerber,et al.  Imaged-based verification of as-built documentation of operational buildings , 2012 .

[31]  Hans Martin Kjer,et al.  Evaluation of surface registration algorithms for PET motion correction , 2010 .

[32]  Carl T. Haas,et al.  Automatic Detection of Cylindrical Objects in Built Facilities , 2014, J. Comput. Civ. Eng..

[33]  Fernanda Leite,et al.  Evaluation of accuracy of as-built 3D modeling from photos taken by handheld digital cameras , 2012 .

[34]  David Arditi,et al.  Time-Lapse Digital Photography Applied to Project Management , 2002 .

[35]  Jochen Teizer.  3D range imaging camera sensing for active safety in construction , 2008, J. Inf. Technol. Constr..

[36]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Patricio A. Vela,et al.  Optimized selection of key frames for monocular videogrammetric surveying of civil infrastructure , 2013, Adv. Eng. Informatics.

[38]  J. Denavit,et al.  A kinematic notation for lower pair mechanisms based on matrices , 1955 .

[39]  H. Son,et al.  Fully Automated As-Built 3D Pipeline Segmentation Based on Curvature Computation from Laser-Scanned Data , 2013 .

[40]  Frédéric Bosché,et al.  Automated progress tracking using 4D schedule and 3D sensing technologies , 2012 .

[41]  S. V. Sreenivasan,et al.  Position error modeling for automated construction manipulators , 2004 .

[42]  Eugeniusz Budny,et al.  Robotics in Civil Engineering , 1988 .

[43]  Frédéric Bosché,et al.  The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components , 2015 .

[44]  Changmin Kim,et al.  Fully automated registration of 3D data to a 3D CAD model for project progress monitoring , 2013 .

[45]  Mani Golparvar-Fard,et al.  Visualization of construction progress monitoring with 4D simulation model overlaid on time-lapsed photographs , 2009 .

[46]  Peter E.D. Love,et al.  Evaluating the direct and indirect costs of rework in construction , 2012 .

[47]  Frédéric Bosché,et al.  Plane-based registration of construction laser scans with 3D/4D building models , 2012, Adv. Eng. Informatics.

[48]  David Arditi,et al.  Total quality management in the construction process , 1997 .

[49]  Ying Wang,et al.  Integrating Augmented Reality with Building Information Modeling: Onsite construction process controlling for liquefied natural gas industry , 2014 .

[50]  Frédéric Bosché,et al.  Automating surface flatness control using terrestrial laser scanning and building information models , 2014 .

[51]  Frédéric Bosché,et al.  Tracking the Built Status of MEP Works: Assessing the Value of a Scan-vs-BIM System , 2014, J. Comput. Civ. Eng..

[52]  Seokho Chi,et al.  Image-Based Safety Assessment: Automated Spatial Safety Risk Identification of Earthmoving and Surface Mining Activities , 2012 .

[53]  Thomas Bock.  Construction robotics , 2007, Auton. Robots.

[54]  Miroslaw J. Skibniewski,et al.  Automation and Robotics for Road Construction and Maintenance , 1990 .